Damage detection using machine learning

ABSTRACT

Systems and methods for detecting hail damage on a vehicle are described including, receiving an image of at least a section of a vehicle. Detecting a plurality of hail damage including, detecting a plurality of damaged areas distributed over the entire section of the vehicle, and differentiating the plurality of damaged areas from one or more areas of noise, processing the received image to classify one or more sections of the vehicle as one or more panels of the vehicle bodywork, and using the detected areas of damage, the classification of the seriousness of the damage and the classification of one or more panels to compute a panel damage density estimate.

BACKGROUND

Repairing damage to bodywork is a common task undertaken by repair shopsand garages world-wide. In the mid-western US alone approximately 20000insurance claims relating to hail damage are filed every year. Repairshops therefore need to assess damage associated with hail storms andother body-work damage in an efficient manner.

Current damage detection techniques rely on a combination of classicalcomputer vision techniques, which often requires bulky and expensivecamera rigs. A hybrid 3D optical scanning system can be used for damagedetection. An example 3D system may combine methodologies of activestereo 3D reconstruction and deflectometry to provide accurate 3Dsurface measurements of an object under inspection. Such techniquesrequire a relatively long inspection time and are limited in theirability to detect all damage on a portion of a vehicle, especially atthe edges of panels. As the accuracy is not sufficiently reliable manualverification is required, which further increases the time required tocomplete an inspection.

There is therefore a need for automated damage detection that is fasterand more accurate than current methods.

SUMMARY

This specification describes a neural network system implemented ascomputer programs on one or more computers in one or more locations thatis configured process images to generate a damage density estimate.

In a first aspect there is provided a method including, receiving animage of at least a section of a vehicle, processing the received imageusing damage detection neural network to detect a plurality of haildamage areas on the section of the vehicle and to classify each of theplurality of areas of damage according to the seriousness of the damage,wherein, detecting a plurality of hail damage includes, detecting aplurality of damaged areas distributed over the entire section of thevehicle, and differentiating the plurality of damaged areas from one ormore areas of noise, processing the received image using a furtherneural network to classify one or more sections of the vehicle as one ormore panels of the vehicle bodywork, and using the detected areas ofdamage, the classification of the seriousness of the damage and theclassification of one or more panels to compute a panel damage densityestimate.

In an implementation, the received image is selected from one of amonocular image or a stereo image.

In an implementation, prior to processing the received image using thefurther neural network, generating an input to the further neuralnetwork from the received image by converting the received image to abinary image.

In an example, processing the converted image using a generator neuralnetwork to generate a modified image, wherein the generator neuralnetwork has been trained jointly with a discriminator neural network togenerate modified images that have reduced image noise relative to inputimages to the generator neural network.

In an implementation, the plurality of areas of damage are one or moredents in the panels of the vehicle bodywork.

In an example, the seriousness of the damage is classified according tothe depth and density of the dents.

In an implementation the first neural network is trained on a data setcomprising a mix of image formats, and wherein the further neuralnetwork is trained on a data set comprising a mix of image formats.

In an example, the mix of image formats include one or more 3D geometryfiles.

In an example, the one more 3D geometry files are augmented withsimulated hail damage, the simulated hail damage simulated using hailimpact analysis.

In an implementation, detecting a plurality of hail damage furthercomprises differentiating the plurality of damaged areas from one ormore sources of noise.

In an example, the one or more sources of noise are selected from dustparticles, dirt and specular reflection.

In a second aspect there is provided a system including, one or morecameras,

one or more computing devices, and one or more storage devices storinginstructions that when executed by the one or more computers cause theone or more computers to perform operations comprising, receiving animage of at least a section of a vehicle, processing the received imageusing damage detection neural network to detect a plurality of haildamage areas on the section of the vehicle and to classify each of theplurality of areas of damage according to the seriousness of the damage,wherein, detecting a plurality of hail damage comprises, detecting aplurality of damaged areas distributed over the entire section of thevehicle, and differentiating the plurality of damaged areas from one ormore areas of noise, processing the received image using a furtherneural network to classify one or more sections of the vehicle as one ormore panels of the vehicle bodywork, and

using the detected areas of damage, the classification of theseriousness of the damage and the classification of one or more panelsto compute a panel damage density estimate.

In an implementation, the one or more cameras comprise at least twocameras and the image is a composite stereo 3D image generated from theat least two cameras

In an implementation, the one or more cameras comprise a cell-phonecamera.

In an implementation, one or more cameras are located at a firstlocation and the one or more computing and one or more storage devicesare located at a second location.

In a third aspect there is provided one or more non-transitorycomputer-readable media storing instructions that when executed by oneor more computers cause the one or more computers to perform operationscomprising, receiving an image of at least a section of a vehicle,processing the received image using damage detection neural network todetect a plurality of hail damage areas on the section of the vehicleand to classify each of the plurality of areas of damage according tothe seriousness of the damage, wherein, detecting a plurality of haildamage comprises, detecting a plurality of damaged areas distributedover the entire section of the vehicle, and differentiating theplurality of damaged areas from one or more areas of noise, processingthe received image using a further neural network to classify one ormore sections of the vehicle as one or more panels of the vehiclebodywork, and using the detected areas of damage, the classification ofthe seriousness of the damage and the classification of one or morepanels to compute a panel damage density estimate.

In a fourth aspect, there is provided a method comprising, receiving animage of at least a section of a vehicle, generating an input to aneural network from the received image by converting the received imageto a binary image, processing the converted image using a generatorneural network to generate a modified image, wherein the generatorneural network has been trained jointly with a discriminator neuralnetwork to generate modified images that have reduced image noiserelative to input images to the generator neural network, and processingthe converted image using a further neural network to classify one ormore sections of the vehicle as one or more panels of the vehiclebodywork.

In an implementation, the received image is a CAD image.

In an implementation, the one more CAD images are augmented withsimulated hail damage, the simulated hail damage simulated using hailimpact analysis.

In an implementation, the method includes using the processed image asinput to a damage detection neural network.

In an implementation, the modified image is used as a training input toone or more of a damaged detection neural network and a neural networkto classify one or more sections of the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flow diagram of an example process for damage densitydetermination.

FIG. 2 is a schematic diagram of an example of dent detection

FIG. 3 is a schematic diagram of an example of panel detection.

FIG. 4 is a schematic diagram of an example system for reducing noise inimages for damage detection.

FIG. 5 is a flow diagram of an example method for generating images in astandard format for hail damage analysis.

FIG. 6 is an example image used to train a damage detection neuralnetwork.

FIG. 7 is a schematic diagram of an example system for image processing.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 shows a flow diagram of an example process 100 for damagedetection. The process 200 is performed by an appropriately programmedsystem of one or more computers located in one or more locations, e.g.,a system for detecting damage, the system for detecting damage, receives102 an image of at least a section of a vehicle. The image can be animage of a part or parts of a vehicle, e.g. fender, door, wheel archetc. The image may be a three-dimensional stereo image, monocular imageor other appropriate image.

The system processes 104 the received image to detect a plurality ofhail damage areas on the section of the vehicle and to classify each ofthe plurality of areas of damage according to the seriousness of thedamage. Detecting the plurality of hail damage areas can includedetecting a plurality of damaged areas distributed over the entiresection of the vehicle and differentiating the plurality of damagedareas from one or more areas of noise, the detection may be done using aneural network or other machine learning technique. Sources of noiseinclude dust particles, dirt, specular reflection, flaws in paint etc.

The system further processes 106 the received image using a furtherneural network to classify one or more sections of the vehicle as one ormore panels of the vehicle bodywork.

The system can use the detected areas of damage, the classification ofthe seriousness of the damage and the classification of one or morepanels to compute 108 a panel damage density estimate (see, for example,table 1).

TABLE 1 Dent size classification Total per Density Panel type SmallMedium Large Panel per panel Left Front 200 20 25 245 0.314 Door LeftRail 5 25 15 45 0.058 Left Front 56 7 9 72 0.092 Fender

In some implementations, the received image may be pre-processed togenerate a standard image, for example a binary image. The binary imagemay also have noise removed. Pre-processing may include converting theimage using a generator neural network to generate a modified image,wherein the generator neural network has been trained jointly with adiscriminator neural network to generate modified images that havereduced image noise relative to input images to the generator neuralnetwork. Pre-processing is described in more detail with respect to FIG.4 and FIG. 5 below.

In an implementation, the system processes the received image using aneural network, for example, a masked R-CNN. Although a masked RCNN isone example of an appropriate technique for classifying hail damageaccording the size and seriousness of the damage, the skilled personwill be aware that other implementations are possible, for example,other neural network implementations e.g. Fast RCNN, Yolo, or non-neuralnetwork based Machine Learning techniques e.g. random forests, gradientboosting etc.

A masked R-CNN is a deep neural network. It consists of a bottom-uppathway, a top-bottom pathway and lateral connections. The bottom-uppathway can be any convolutional neural network which extracts featuresfrom raw images e.g. ResNet, VGG etc. The top-bottom pathway (e.g.forward pass) generates a feature map. The forward pass of the CNNresults in feature maps at different layers i.e. builds s multi-levelrepresentation at different scales. Top-down features are propagated tohigh resolution feature maps thus having features across all levels TheLateral connections are convolution and adding operations between twocorresponding levels of the two pathways.

The masked R-CNN proposes regions of interest in a feature map by usinga selective search to generate region proposals for each image using aRegion Prediction Network (RPN).

In some examples, the masked R-CNN uses a region of interest poolinglayer to extract feature maps from each region of interest and performsclassification and bounding box detection on the extracted feature maps.The pooling layer converts each variable size region of interest into afixed size to be fed to a connected layer by performing segmentation andpooling e.g. max-pooling. Bounding box regression is used to refine thebounding boxes such that class labels and bounding boxes are predicted.

In other examples, the R-CNN uses a region of interest alignment layer.The region of interest alignment layer takes the proposed region ofinterest and dividing it into a specified number of equal size boxes andapplying bilinear interpolation inside each box to compute the exactvalues of the input features at regularly sampled locations, e.g. 4regularly sampled locations.

The masked R-CNN can further generate a segmentation mask. Anintersection over union (IoU) is computed for each bounding box with aground truth bounding box. Where the IoU of a bounding box with a groundtruth bounding box is greater than a threshold level the bounding box isselected as a region of interest. The masked R-CNN can then furtherencode a binary mask per class for each region of interest.

Using the above approach a plurality of areas of hail damage can bedetected on a received image of at least a section of a vehicle usingthe masked R-CNN. For example, the plurality of bounding boxes can beused to generate a count of the number of detections of hail damage inthe received image and the size of the bounding box may be used togenerate an estimate of the seriousness of each area of hail damage.Each area of hail damage can further be labelled as e.g. slight,moderate, severe. Alternatively the damage can be labelled as small,medium, large etc. The binary mask for each region can be used tocompute an overall area effected.

In an implementation, the system can use a masked R-CNN to generate arespective classification of one or more sections of the vehicle. Themasked R-CNN extracts a feature map from the image and executes aregression such that bounding boxes and class labels are extracted fromthe feature map and the generates a mask is generated that identifiesthe damaged section of the vehicle. More generally, however, anyappropriate machine learning model can be used to perform theclassification.

In an implementation, the system may take the detected panel from theclassification of the one or more panel and use the make, model and yearof the vehicle to determine the dimensions of the identified panel. Thepercentage of damage of the identified panel can then be computed and aproportion of the damage that is slight, moderate and severe can beidentified. Using the damage density estimate a user of the system cantherefore determine whether it is cost effective to repair the panel orwhether the panel should be replaced.

In an implementation a first neural network (arranged to detect aplurality of areas of hail damage) and a further neural network(arranged to identify one or more panels in an image of a section of avehicle) are trained on a data set including a mix of image formats. Theimages can be annotated with training labels, for example, the traininglabels can be provided in COCO format. COCO is large scale images withCommon Objects in Context (COCO) for object detection, segmentation, andcaptioning data set. The mix of image formats may include one or more3-D geometry files e.g. CAD files. The 3-D geometry files may beaugmented with hail damage simulated using impact analysis. Impactanalysis make be executed using an appropriate simulation method e.g.finite element analysis. Examples of commercially available products forfinite element analysis include Ansys (Ansys, Inc.) and Abaqus (DassaultSystemes).

The hail damage may be simulated under different lighting conditions,lighting conditions can be simulated using appropriate lightingtechniques, e.g. ray-tracing, ray-casting etc.

FIG. 2 is a schematic diagram 200 of an example of dent detection.

An input image 202 is received. In some implementations the input image202 may have undergone various pre-processing steps prior to being inputinto the dent detection system. For example, the image may bepre-processed to remove background features, remove noise, convert theimage to greyscale etc. The received image can be converted to a binaryimage prior to undergoing processing. The received image may be selectedfrom a stereo image or a monocular image. In some implementations theimage may be an image from one or more high resolution cameras. In otherimplementations the image may be an image from one or more consumergrade cameras e.g. a cell-phone camera, D-SLR or other appropriatecamera.

The image undergoes processing at a machine learning system 204, e.g. asystem that performs the method of processing images described withreference to FIG. 1. The system having been previously trained using atraining data set that is labelled and/or masked to train the system todetect a plurality of regions of hail damage. In an implementation theimage can be converted into a feature map and undergo one or moreconvolutions and/or regressions to output one or more classificationsand one or more bounding boxes and/or masks associated with one or moredetected areas of hail damage in the input image. An output image 206may be generated which indicates the one or more identified areas ofhail damage on the received input image.

FIG. 3 is a schematic diagram 300 of panel detection.

An input image 302 is received. In some implementations the input image302 may have undergone various pre-processing steps prior to being inputinto the dent detection system. For example, the image can bepre-processed to remove background features, remove noise, convert theimage to greyscale etc. The received image can be converted to a binaryimage prior to undergoing processing. The received image can be selectedfrom a stereo image or a monocular image. In some implementations theimage may be an image from one or more high resolution cameras. In otherimplementations the image may be an image from one or more consumergrade cameras e.g. a cell-phone camera, D-SLR or other appropriatecamera.

The image under goes processing at a machine learning system 304, e.g. asystem that performs the method of processing images described withreference to FIG. 1. The system having been previously trained using atraining data set that is labelled and/or masked to train the system todetect panels of a vehicle. In an implementation the image can beconverted into a feature map and undergo one or more convolutions and/orregressions to output one or more classifications and one or morebounding boxes and/or masks associated with panel classifications in theimage. An output image 306 can be generated which indicates e.g. using amask generated by the neural network one or more identified panels inthe image.

FIG. 4 is a schematic diagram of an example system 400 for reducingnoise in images for damage detection.

The system may use a Generative Adversarial Network (GAN). In a GAN, twoneural networks are used in competition. The generator network 402receives an input image 404 and generates a new output image 406 fromthe input image using learned parameters. The output image 406 is passedto a discriminator network 408 to predict whether the output image 406has been generated by the generator network 404 or is a real image.

During training, the two networks are trained on an objective functionthat causes the two networks to “compete” with each other, the generatornetwork to fool the discriminator network and the discriminator networkto correctly predict which images are real. The generator network istherefore trained to effectively perform a specific task, e.g. removenoise from an image. In an initial training phase the training data setmay be, for example a set of images to which noise has been artificiallyadded. The training data set may include noise and/or artifacts that areassociated with dents and/or panels and may also include noise and/orartifacts that are not associated with any panels.

The generator network 402 can be trained to remove the noise from theimage. The discriminator network 408 can predict whether the image is animage output by the generator network 402 or whether it is a targetimage 410 from the ground truth data set, i.e., an image from the dataset that includes the images prior to the noise being artificiallyadded. A first comparison can be made between the target image 410 andthe output image 406 and a second comparison can be made between thetarget image 410 and the prediction of the discriminator network 408.The comparisons can be passed to an optimizer 412 which updates theweights 414 of the generator network and the discriminator neuralnetwork to optimize a GAN objective.

In a first implementation the GAN objective may comprise finding anequilibrium between the two networks {Generator (G) and Discriminator(D)} by solving a minimax equation as indicated below:

${\min\limits_{\theta}\mspace{14mu}{\max\limits_{\phi}\mspace{14mu}{V\left( {G_{\theta},D_{\phi}} \right)}}} = {{{\mathbb{E}}_{x \sim p_{data}}\left\lbrack {\log\mspace{14mu}{D_{\phi}(x)}} \right\rbrack} + {{\mathbb{E}}_{z \sim {p{(z)}}}\left\lbrack {\log\left( {1 - {D_{\phi}\left( {G_{\theta}(z)} \right)}} \right)} \right\rbrack}}$

This equation is known as minimax equation (derived from KL-divergencecriterion) as it is trying to jointly optimize two parameterizednetworks, G (Generator) and D (Discriminator), to find an equilibriumbetween the two. The objective is to maximize the confusion of D whileminimizing the failures of G. When solved, the parameterized, implicit,generative data distribution should match the underlying original datadistribution fairly well.

In a further implementation the goal of the generative model is to comeup with a procedure of matching its generated distribution to a realdata distribution so it can fake the discriminator network. Minimizingthe distance between the two distributions is critical for optimizingthe generator network so it could generate images that are identical toa sample from the original data distribution (p(x)). In order to measurethe difference between the generated data distribution (q(x)) and theactual data distribution (p(x)), there are multiple objective functions.For example, Jensen Shannon Divergence (JSD) {derived fromKullbach-Liebler Divergence (KLD)}, Earth-Mover (EM) distance (AKAWasserstein distance) and Relaxed Wasserstein GAN to name a few.

The trained generator network 402 can then be used to generate de-noisedinput images for input into one or more or of a panel detection neuralnetwork and a dent detection neural network. The images can be, forexample, a set comprising a mix of image formats, and wherein thefurther neural network is trained on a data set comprising a mix ofimage formats. The output image are de-noised binary images.

FIG. 5 is a flow diagram of an example method 500 for generating imagesin a standard format for hail damage analysis. The method 500 may beexecuting using, for example, a GAN network, as described with referenceto FIG. 5 above. The method may be used to generate images in a standardformat for training a damage detection neural network and/or a paneldetection neural network as described above. Alternatively it may beused to increase the quality of images fed to a network during aprediction phase. For example, reducing the amount of noise and/orsources of confusion in the image e.g. noise dust particles, dirt andspecular reflection,

An input image is received 502, the input image comprising an image ofat least a section of a vehicle. The input image may include a pluralityof damaged areas distributed over the entire section of the vehicle, theimage may also include one or more areas of noise. In an implementationthe input image may be a 3D geometry file, for example a CAD file thatincludes one or more simulated areas of damage.

An input to a neural network may be generated by converting the receivedimage to a binary image 504,

The converted image can be processed 506 using a generator neuralnetwork to generate a modified image, wherein the generator neuralnetwork has been trained jointly with a discriminator neural network togenerate modified images that have reduced image noise relative to inputimages to the generator neural network.

The output images may be used as input to a machine learning system todetect hail damage. The machine learning system to detect hail damageprocessing the converted image using a further neural network toclassify one or more sections of the vehicle as one or more panels ofthe vehicle bodywork. The machine learning system can further detect aplurality of damaged areas distributed over the entire section of thevehicle and differentiate the plurality of damaged areas from one ormore areas of noise.

FIG. 6 is an example of a 3D geometry file (e.g. a CAD file) 600 withgenerated hail dents as used to train one or more of the machinelearning systems described above.

FIG. 7 is a schematic diagram of an example system 700 for imageprocessing.

The system may include one or more cameras, e.g. camera 702 andsmartphone camera 704. As noted above the cameras may be one or morespecialist or high resolution cameras e.g. active or passive stereovision cameras, Gigapixel monocular or single vision cameras etc. Inother implementations the one or more consumer grade cameras e.g.smartphone camera 704, DSLR camera etc. In some implementations acombination of specialist and consumer grade cameras may be used.

The one or more cameras can be connected to one or more computingdevices, e.g. laptop 704 or terminal 708 via a network 710. The network710 can be a wired or wireless network. The cameras may be enabled totransfer images via a wireless network, e.g. cellular network, wireless,Bluetooth, NFC or other standard wireless network protocol. The camerasmay alternatively or additionally be enabled to transfer the images viaa wired network, e.g. via a computer network cable (e.g. CAT 5, 6 etc.),USB cable. Alternatively or additionally the images can be transferredvia other physical media; flash drive, memory card, CD, DVD etc.

The system may further include one or more storage devices, e.g. storagedevice 712 which is arranged to store instructions. Storage device 712may be a separate storage device, e.g. external storage, cloud storageetc. or may be storage that is internal to the computing device.

When the instructions stored at storage device 712 are executed by oneor more computers 704, 708 the instructions cause the one or morecomputing devices to perform operations including; receive an image ofat least a section of a vehicle, process the received image using damagedetection neural network to detect a plurality of hail damage areas onthe section of the vehicle and to classify each of the plurality ofareas of damage according to the seriousness of the damage, process thereceived image using a further neural network to classify one or moresections of the vehicle as one or more panels of the vehicle bodywork;and compute a panel damage density estimate, as described in more detailabove with reference to FIG. 1. The system set out in FIG. 7 may furtherbe implemented to execute any of the foregoing methods.

The one or more computing devices may be arranged to execute theinstructions in serial, i.e. on a single processor at a single at asingle computing device, or in parallel, i.e. on a plurality ofprocessors located on one or more computing devices.

Other embodiments and modifications of the present invention will bereadily apparent to those of ordinary skill in the art having thebenefit of the teachings presented in the foregoing description anddrawings.

What is claimed is:
 1. A method comprising: receiving an image of atleast a section of a vehicle; processing the received image using damagedetection neural network to detect a plurality of hail damage areas onthe section of the vehicle and to classify each of the plurality ofareas of damage according to the seriousness of the damage; wherein,detecting a plurality of hail damage comprises: detecting a plurality ofdamaged areas distributed over the entire section of the vehicle; anddifferentiating the plurality of damaged areas from one or more areas ofnoise; processing the received image using a further neural network toclassify one or more sections of the vehicle as one or more panels ofthe vehicle bodywork; and using the detected areas of damage, theclassification of the seriousness of the damage and the classificationof one or more panels to compute a panel damage density estimate.
 2. Themethod according to claim 1 wherein the received image is selected fromone of a monocular image or a stereo image.
 3. The method according toclaim 1 comprising, prior to processing the received image using thefurther neural network, generating an input to the further neuralnetwork from the received image by converting the received image to abinary image.
 4. The method according to claim 3 comprising processingthe converted image using a generator neural network to generate amodified image, wherein the generator neural network has been trainedjointly with a discriminator neural network to generate modified imagesthat have reduced image noise relative to input images to the generatorneural network.
 5. The method according to claim 1 wherein the pluralityof areas of damage are one or more dents in the panels of the vehiclebodywork.
 6. The method according to claim 5 wherein the seriousness ofthe damage is classified according to the depth and density of thedents.
 7. The method according to claim 1 wherein the first neuralnetwork is trained on a data set comprising a mix of image formats, andwherein the further neural network is trained on a data set comprising amix of image formats.
 8. The method according to claim 7 wherein the mixof image formats include one or more 3D geometry files.
 9. The methodaccording to claim 8 wherein the one more 3D geometry files areaugmented with simulated hail damage, the simulated hail damagesimulated using hail impact analysis.
 10. The method according to claim1 wherein detecting a plurality of hail damage further comprisesdifferentiating the plurality of damaged areas from one or more sourcesof noise.
 11. The method according to claim 10 wherein the one or moresources of noise are selected from dust particles, dirt and specularreflection.
 12. A system comprising: one or more cameras; one or morecomputing devices; and one or more storage devices storing instructionsthat when executed by the one or more computers cause the one or morecomputers to perform operations comprising: receiving an image of atleast a section of a vehicle; processing the received image using damagedetection neural network to detect a plurality of hail damage areas onthe section of the vehicle and to classify each of the plurality ofareas of damage according to the seriousness of the damage; wherein,detecting a plurality of hail damage comprises: detecting a plurality ofdamaged areas distributed over the entire section of the vehicle; anddifferentiating the plurality of damaged areas from one or more areas ofnoise; processing the received image using a further neural network toclassify one or more sections of the vehicle as one or more panels ofthe vehicle bodywork; and using the detected areas of damage, theclassification of the seriousness of the damage and the classificationof one or more panels to compute a panel damage density estimate. 13.The system of claim 12 wherein the one or more cameras comprise at leasttwo cameras and the image is a composite stereo 3D image generated fromthe at least two cameras
 14. The system of claim 12 wherein the one ormore cameras comprise a cell-phone camera.
 15. The system of claim 12wherein the one or more cameras are located at a first location and theone or more computing and one or more storage devices are located at asecond location.
 16. One or more non-transitory computer-readable mediastoring instructions that when executed by one or more computers causethe one or more computers to perform operations comprising: receiving animage of at least a section of a vehicle; processing the received imageusing damage detection neural network to detect a plurality of haildamage areas on the section of the vehicle and to classify each of theplurality of areas of damage according to the seriousness of the damage;wherein, detecting a plurality of hail damage comprises: detecting aplurality of damaged areas distributed over the entire section of thevehicle; and differentiating the plurality of damaged areas from one ormore areas of noise; processing the received image using a furtherneural network to classify one or more sections of the vehicle as one ormore panels of the vehicle bodywork; and using the detected areas ofdamage, the classification of the seriousness of the damage and theclassification of one or more panels to compute a panel damage densityestimate.
 17. A method comprising: receiving an image of at least asection of a vehicle; generating an input to a neural network from thereceived image by converting the received image to a binary image;processing the converted image using a generator neural network togenerate a modified image, wherein the generator neural network has beentrained jointly with a discriminator neural network to generate modifiedimages that have reduced image noise relative to input images to thegenerator neural network; and processing the converted image using afurther neural network to classify one or more sections of the vehicleas one or more panels of the vehicle bodywork.
 18. The method of claim17 wherein the received image is a CAD image.
 19. The method of claim 18wherein the one more CAD images are augmented with simulated haildamage, the simulated hail damage simulated using hail impact analysis.20. The method of claim 17 further comprising using the processed imageas input to a damage detection neural network.
 21. The method of claim17 wherein the wherein the modified image is used as a training input toone or more of a damaged detection neural network and a neural networkto classify one or more sections of the vehicle.